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Figure S1: LNCaP, VCaP and DuCaP cells were grown in charcoal-dextran stripped serum (CSS) for 48 h and treated with increasing concentrations of R1881 or DHT in the presence or absence of Enzalutamide (10 µM) or vehicle (Ctrl) for 48 h. Figure S2: Androgens increased lipid uptake of long-chain fatty acids. LAPC4 cells were grown in CSS for 48 h and treated with 1 nM R1881 or vehicle for 48 h. Figure S3: (A) LNCaP cells were synchronized in G0/G1 by androgen deprivation (CSS for 48 h) followed by treatment with Tunicamycin (1 mg/mL), Hydroxyurea (1 M), or Nocodazole (25 ug/mL) for another 24 h, placing cell cycle blocks in G0/G1, S phase and mitosis, respectively. Figure S4: Oncomine analysis of candidate lipid transporters in (A) Grasso [2], (B) Varambally [3] and (C) LaTulippe datasets [4] comparing gene expression of normal prostate gland versus localized, primary prostate cancer tumor samples. Figure S5: (A) DuCaP (top panel) and VCaP cells (bottom panel) were grown in CSS for 48 h and treated with 10 nM DHT in the absence or presence of Enz (10 µM) or vehicle (Ctrl) for 48 h. Table S1. Primer sequences

Aging is the primary risk factor for most neurodegenerative diseases, and recently coronavirus disease 2019 (COVID-19) has been associated with severe neurological manifestations that can eventually impact neurodegenerative conditions in the long-term. The progressive accumulation of senescent cellsin vivostrongly contributes to brain aging and neurodegenerative co-morbidities but the impact of virus-induced senescence in the aetiology of neuropathologies is unknown. Here, we show that senescent cells accumulate in physiologically aged brain organoids of human origin and that senolytic treatment reduces inflammation and cellular senescence; for which we found that combined treatment with the senolytic drugs dasatinib and quercetin rejuvenates transcriptomic human brain aging clocks. We further interrogated brain frontal cortex regions in postmortem patients who succumbed to severe COVID-19 and observed increased accumulation of senescent cells as compared to age-matched control brains from non-COVID-affected individuals. Moreover, we show that exposure of human brain organoids to SARS-CoV-2 evoked cellular senescence, and that spatial transcriptomic sequencing of virus-induced senescent cells identified a unique SARS-CoV-2 variant-specific inflammatory signature that is different from endogenous naturally-emerging senescent cells. Importantly, following SARS-CoV-2 infection of human brain organoids, treatment with senolytics blocked viral retention and prevented the emergence of senescent corticothalamic and GABAergic neurons. Furthermore, we demonstrate in human ACE2 overexpressing mice that senolytic treatment ameliorates COVID-19 brain pathology following infection with SARS-CoV-2.In vivotreatment with senolytics improved SARS-CoV-2 clinical phenotype and survival, alleviated brain senescence and reactive astrogliosis, promoted survival of dopaminergic neurons, and reduced viral and senescence-associated secretory phenotype gene expression in the brain. Collectively, our findings demonstrate SARS-CoV-2 can trigger cellular senescence in the brain, and that senolytic therapy mitigates senescence-driven brain aging and multiple neuropathological sequelae caused by neurotropic viruses, including SARS-CoV-2.

Background Single-cell RNA sequencing (scRNA-seq) methods have been advantageous for quantifying cell-to-cell variation by profiling the transcriptomes of individual cells. For scRNA-seq data, variability in gene expression reflects the degree of variation in gene expression from one cell to another. Analyses that focus on cell–cell variability therefore are useful for going beyond changes based on average expression and, instead, identifying genes with homogeneous expression versus those that vary widely from cell to cell. Results We present a novel statistical framework, scShapes, for identifying differential distributions in single-cell RNA-sequencing data using generalized linear models. Most approaches for differential gene expression detect shifts in the mean value. However, as single-cell data are driven by overdispersion and dropouts, moving beyond means and using distributions that can handle excess zeros is critical. scShapes quantifies gene-specific cell-to-cell variability by testing for differences in the expression distribution while flexibly adjusting for covariates if required. We demonstrate that scShapes identifies subtle variations that are independent of altered mean expression and detects biologically relevant genes that were not discovered through standard approaches. Conclusions This analysis also draws attention to genes that switch distribution shapes from a unimodal distribution to a zero-inflated distribution and raises open questions about the plausible biological mechanisms that may give rise to this, such as transcriptional bursting. Overall, the results from scShapes help to expand our understanding of the role that gene expression plays in the transcriptional regulation of a specific perturbation or cellular phenotype. Our framework scShapes is incorporated into a Bioconductor R package (https://www.bioconductor.org/packages/release/bioc/html/scShapes.html).

BackgroundSingle-cell RNA-sequencing (scRNA-seq) technologies enable the capture of gene expression heterogeneity and consequently cell-to-cell variability at the cell type level. Although different methods have been proposed to quantify cell-to-cell variability, it is unclear what the optimal statistical approach is, especially in light of challenging data structures that are unique to scRNA-seq data like zero inflation.ResultsIn this study, we conducted a systematic evaluation of cell-to-cell gene expression variability using 14 different variability metrics that are commonly applied to transcriptomic data. Performance was evaluated with respect to data-specific features like sparsity and sequencing platform, biological properties like gene length, and the ability to recapitulate true levels of variability based on simulation and known biological gene sets like ribosomal genes and stably expressed genes. scran had the strongest all-round performance, and this metric was then applied to investigate the changes in cell-to-cell variability that occur during ageing. Studying ageing showcases the value of cell-to-cell variability as it is a genetically-regulated program that is influenced by stochastic processes.scRNA-seq datasets from hematopoietic stem cells (HSCs) and B lymphocytes and other cell types from this differentiation lineage were used with scran to identify the genes with consistent patterns of variable and stable expression profiles during differentiation. Furthermore, to understand the regulatory relationship for genes that were differentially-variable in their expression between young and old mice, we constructed networks using transcription factors and their known targets for HSC and B lymphocyte cells. Comparisons of these networks identified a shared TFSfpi1that although was seen to increase in gene expression variability in old mice versus young in both cell types, the corresponding targets were distinct and their gene expression variability had different directions between cell types.ConclusionsThrough these analyses, we highlight the importance of capturing cell-to-cell gene expression variability in a complex biological process like differentiation and ageing, and emphasise the value and specificity of interpreting these findings at the level of individual cell types.

Metastasis is orchestrated by phenotypic and metabolic reprogramming underlying tumor aggressiveness. Redox signaling by mammary tumor knockdown (KD) of the antioxidant glutathione peroxidase 2 (GPx2) enhanced metastasis via dynamic changes in epithelial-to-mesenchymal transition. Single cell RNA sequencing (scRNA-seq) of the control and PyMT/GPx2 KD mammary tumor revealed six luminal and one basal/mesenchymal like (cluster 3) subpopulations. Remarkably, GPx2 KD enhanced the size and basal/mesenchymal gene signature of cluster 3 as well as induced epithelial/mesenchymal (E/M) clusters which expressed markers of oxidative phosphorylation and glycolysis, indicative of hybrid metabolism. These data were validated in human breast cancer xenografts and were supported by pseudotime cell trajectory analysis. Moreover, the E/M and M states were both attenuated by GPx2 gain of function or HIF1α inhibition, leading to metastasis suppression. Collectively, these results demonstrate that redox/HIF1α signaling promotes mesenchymal gene expression, resulting in E/M clusters and a mesenchymal root subpopulation, driving phenotypic and metabolic heterogeneity underlying metastasis.SignificanceBy leveraging single cell RNA analysis, we were able to demonstrate that redox signaling by GPx2 loss in mammary tumors results in HIF1α signaling, which promotes partial and full EMT conversions, represented by distinct tumor cell subpopulations, which in turn express hybrid and binary metabolic states. These data underscore a phenotypic and metabolic co-adaptation in cancer, arguing in favor of the GPx2-HIF1α axis as a therapeutic platform for targeting tumor cell metastasis.

Accurately identifying cell populations is paramount to the quality of downstream analyses and overall interpretations of single-cell RNA-seq (scRNA-seq) datasets but remains a challenge. The quality of single-cell clustering depends on the proximity metric used to generate cell-to-cell distances. Accordingly, proximity metrics have been benchmarked for scRNA-seq clustering, typically with results averaged across datasets to identify a highest performing metric. However, the ‘best-performing’ metric varies between studies, with the performance differing significantly between datasets. This suggests that the unique structural properties of a scRNA-seq dataset, specific to the biological system under study, has a substantial impact on proximity metric performance. Previous benchmarking studies have omitted to factor the structural properties into their evaluations. To address this gap, we developed a framework for the in-depth evaluation of the performance of 17 proximity metrics with respect to core structural properties of scRNA-seq data, including sparsity, dimensionality, cell population distribution and rarity. We find that clustering performance can be improved substantially by the selection of an appropriate proximity metric and neighbourhood size for the structural properties of a dataset, in addition to performing suitable pre-processing and dimensionality reduction. Furthermore, popular metrics such as Euclidean and Manhattan distance performed poorly in comparison to several lessor applied metrics, suggesting the default metric for many scRNA-seq methods should be re-evaluated. Our findings highlight the critical nature of tailoring scRNA-seq analyses pipelines to the system under study and provide practical guidance for researchers looking to optimise cell similarity search for the structural properties of their own data.

Identifying the molecular regulators that control ageing is challenging because the ageing process is influenced by a combination of genetic and environmental factors which makes it difficult to source the contribution of a single gene. Multiple studies have demonstrated that as humans age, increased gene expression heterogeneity results in the dysregulation of key regulators and pathways. Given the dynamic nature of gene expression, it is vital that this data be modelled by statistical approaches that can appropriately account for changes in variability to understand the contribution of heterogeneity during the aging process and properly identify its regulators. This study demonstrates the utility of using mixture models to model biological variability of gene expression occurring during ageing and how novel potential regulators of ageing can be identified.Our mixture modelling approach was applied to gene expression data from the Genotype-Tissue Expression (GTEx) cohort. For every gene, the expression profile was modelled using a mixture model across the cohort where the subset of donors corresponding to each mode was tested for a significant change in age group. The multi-tissue aspect of GTEx was leveraged to find ageing regulators based on this mixture model approach genes that were common across multiple tissues, suggesting that the regulation of ageing may also be controlled through a set of genes that have non-tissue-specific activity. Our approach identified the common ageing regulator mTOR, as significantly associated with a mixture model profile but this gene was not detected through standard differential expression analysis using edgeR. Genes identified by the standard approaches like edgeR and the mixture model-based approach were found to be enriched for similar biological pathways. This suggests that while the specific ageing regulators identified from our approach may be distinct, they generally belong in the same pathways as the genes that are identified by standard approaches. Overall, these results indicate that modelling gene expression variability using mixture models in conjunction with standard differential gene expression can help uncover new regulators that have a potential role for understanding human ageing.

BackgroundMetabolic reprograming, non-mutational epigenetic changes, increased cell plasticity and multidrug tolerance are early hallmarks of therapy resistance in cancer. In this temporary, therapy-tolerant state, cancer cells are highly sensitive to ferroptosis, a form of regulated cell death that is caused by oxidative stress through excess levels of iron-dependent peroxidation of polyunsaturated fatty acids (PUFA). However, mechanisms underpinning therapy-induced ferroptosis hypersensitivity remain to be elucidated.MethodsWe used quantitative single cell imaging of fluorescent metabolic probes, transcriptomics, proteomics and lipidomics to perform a longitudinal analysis of the adaptive response to androgen receptor-targeted therapies (androgen deprivation and enzalutamide) in prostate cancer (PCa).ResultsWe discovered that cessation of cell proliferation and a robust reduction in bioenergetic processes were associated with multidrug tolerance and a strong accumulation of lipids. The gain in lipid biomass was fueled by enhanced lipid uptake through cargo non-selective (macropinocytosis, tunneling nanotubes) and cargo-selective mechanisms (lipid transporters), whereasde novolipid synthesis was strongly reduced. Enzalutamide induced extensive lipid remodeling of all major phospholipid classes at the expense of storage lipids, leading to increased desaturation and acyl chain length of membrane lipids. The rise in membrane PUFA levels enhanced membrane fluidity and lipid peroxidation, causing hypersensitivity to glutathione peroxidase (GPX4) inhibition and ferroptosis. Combination treatments against AR and fatty acid desaturation, lipase activities or growth medium supplementation with antioxidants or PUFAs altered GPX4 dependence. Despite multidrug tolerance, PCa cells displayed an enhanced sensitivity to inhibition of lysosomal processing of exogenous lipids, highlighting an increased dependence on lipid uptake in the therapy-tolerant state.ConclusionsOur work provides mechanistic insight into processes of lipid metabolism that underpin the acquisition of therapy-induced GPX4 dependence and ferroptosis hypersensitivity to standard of care therapies in PCa. It demonstrated novel strategies to suppress the therapy-tolerant state that may have potential to delay and combat resistance to androgen receptor-targeted therapies, a currently unmet clinical challenge of advanced PCa. Since enhanced GPX4 dependence is an adaptive phenotype shared by several types of cancer in response to different therapies, our work might have universal implications for our understanding of metabolic events that underpin resistance to cancer therapies.

Industrial Internet of Things (IIoT) is an architecture that facilitates the feasibility of the distributed control of the modernized industrial systems mainly through the Internet of Things and cloud computing. This article proposes an optimal scheduling framework for the real-time operation of smart microgrids in the IIoT environment using an average consensus-based algorithm. The introduced framework suggests a fog layer as a complementary layer of IIoT to reduce latency and provide local computation and data storage for the proposed industry. Security of the system against probable attacks is the other concern that should be observed. To this end, this article sets out to evaluate the impact of a particular type of attack called the denial of service on the performance of the proposed method. Accuracy, feasibility, and fast response of the scheme are demonstrated through simulation results on a microgrid test system in the presence of dispatchable and nondispatchable generation units with heterogeneous devices.

Employment of a specific technology in the conversion of wind energy to electrical power highly influences the cost and reliability of power generation. To help the selection of the best drivetrain technology, this article presents a thorough review of three major wind turbine drivetrains, namely gearbox, direct drive, and hydrostatic. Although the gearbox drivetrain has offered a mature and applicable technology, some inherent difficulties and manufacturing of specific generators and advancements in fluid power machinery resulted in new technologies that are true competitors of the gearbox. The cost, performance, and functionality of these drivetrains are now comparable with the gearbox peers. This article presents a comprehensive review of three different types of the drivetrain and their generators. This article particularly helps researchers and the engineers in the field with new insight into all technologies.

Cellular senescence is characterised by a state of permanent cell cycle arrest. It is accompanied by often variable release of the so-called senescence-associated secretory phenotype (SASP) factors, and occurs in response to a variety of triggers such as persistent DNA damage, telomere dysfunction, or oncogene activation. While cellular senescence is a recognised driver of organismal ageing, the extent of heterogeneity within and between different senescent cell populations remains largely unclear. Elucidating the drivers and extent of variability in cellular senescence states is important for discovering novel targeted seno-therapeutics and for overcoming cell expansion constraints in the cell therapy industry. Here we combine cell biological and single cell RNA-sequencing approaches to investigate heterogeneity of replicative senescence in human ESC-derived mesenchymal stem cells (esMSCs) as MSCs are the cell type of choice for the majority of current stem cell therapies and senescence of MSC is a recognized driver of organismal ageing. Our data identify three senescent subpopulations in the senescing esMSC population that differ in SASP, oncogene expression, and escape from senescence. Uncovering and defining this heterogeneity of senescence states in cultured human esMSCs allowed us to identify potential drug targets that may delay the emergence of senescent MSCsin vitroand perhapsin vivoin the future.

Multi-microgrid systems can improve the resiliency and reliability of the power system network. Secure communication for multi-microgrid operation is a crucial issue that needs to be investigated. This paper proposes a multi-controller software defined networking (SDN) architecture based on fog servers in multi-microgrids to improve the electricity grid security, monitoring and controlling. The proposed architecture defines the support vector machine (SVM) to detect the distributed denial of service (DDoS) attack in the storage of microgrids. The information of local SDN controllers on fog servers is managed and supervised by the master controller placed in the application plane properly. Based on the results of attack detection, the power scheduling problem is solved and send a command to change the status of tie and sectionalize switches. The optimization application on the cloud server implements the modified imperialist competitive algorithm (MICA) to solve this stochastic mixed-integer nonlinear problem. The effective performance of the proposed approach using an SDN-based architecture is evaluated through applying it on a multi-microgrid based on IEEE 33-bus radial distribution system with three microgrids in simulation results.

In search of redox mechanisms in breast cancer, we uncovered a striking role for glutathione peroxidase 2 (GPx2) in oncogenic signaling and patient survival. GPx2 loss stimulates malignant progression due to reactive oxygen species/hypoxia inducible factor-α (HIF1α)/VEGFA (vascular endothelial growth factor A) signaling, causing poor perfusion and hypoxia, which were reversed by GPx2 reexpression or HIF1α inhibition. Ingenuity Pathway Analysis revealed a link between GPx2 loss, tumor angiogenesis, metabolic modulation, and HIF1α signaling. Single-cell RNA analysis and bioenergetic profiling revealed that GPx2 loss stimulated the Warburg effect in most tumor cell subpopulations, except for one cluster, which was capable of oxidative phosphorylation and glycolysis, as confirmed by coexpression of phosphorylated-AMPK and GLUT1. These findings underscore a unique role for redox signaling by GPx2 dysregulation in breast cancer, underlying tumor heterogeneity, leading to metabolic plasticity and malignant progression.

This paper develops a robust stochastic optimization model for reverse logistics in closed-loop supply chains. By determining the optimal flow of products using a Chance Constrained Robust Stochastic Programming (CCRSP), it is highlighted how the number of plant openings is influenced by the changes in carbon credit price. To assess the model performance, a set of numerical experiments in different sizes are developed and conducted. The effectiveness of the results are then compared to a proposed Heuristic Hybrid Taguchi PSO (HTPSO) solution algorithm, which underlines the effectiveness of the model. A sensitivity analysis on the carbon emission rate is carried out which underlines the role of Carbon Tax Policy. Finally, a real-life case study within the automotive manufacturing industry is carried out by applying the developed robust stochastic model. From a practical standpoint, the model can potentially be employed to meet the carbon credits that are used for handling the different carbon prices and trade scenarios. Also, it provides insights on how to better manage uncertainties, as well as to reduce the overall emissions in supply chains.

Carbazole compounds are p-type hole-transporting materials (HTMs) useful for perovskite solar cells (PSCs). In this work, we developed a new class of carbazol based HTMs; non-fused 3-D asymmetric structures (S14 and S12) as HTM of PSCs. To the best of our knowledge, there is no report on non-fused HTMs with a high glass transition temperature (Tg = 165 °C), which reduces crystallization and suppresses grain boundaries in glassy film, resulting in long-term durability. Experimental results show that tuning the carbazole moiety in S14 structure has a constructive influence on geometrical alignment, hole mobility, hydrophobicity, stability as well as efficiency. The resultant power conversion efficiency (PCE) of devices were 15.31% and 11.85% for S14 and S12, respectively. Cell efficiency decreases during the first day for all devices. But after this time, the cell efficiency of the device fabricated with S14 remains constant until 1000 h at atmospheric condition without encapsulation while for devices fabricated by S12 and Spiro-OMeTAD the cell efficiency decreases to 75% and 69%.

De novo lipogenesis is a well-described androgen receptor (AR)–regulated metabolic pathway that supports prostate cancer tumor growth by providing fuel, membrane material, and steroid hormone precursor. In contrast, our current understanding of lipid supply from uptake of exogenous lipids and its regulation by AR is limited, and exogenous lipids may play a much more significant role in prostate cancer and disease progression than previously thought. By applying advanced automated quantitative fluorescence microscopy, we provide the most comprehensive functional analysis of lipid uptake in cancer cells to date and demonstrate that treatment of AR-positive prostate cancer cell lines with androgens results in significantly increased cellular uptake of fatty acids, cholesterol, and low-density lipoprotein particles. Consistent with a direct, regulatory role of AR in this process, androgen-enhanced lipid uptake can be blocked by the AR-antagonist enzalutamide, but is independent of proliferation and cell-cycle progression. This work for the first time comprehensively delineates the lipid transporter landscape in prostate cancer cell lines and patient samples by analysis of transcriptomics and proteomics data, including the plasma membrane proteome. We show that androgen exposure or deprivation regulates the expression of multiple lipid transporters in prostate cancer cell lines and tumor xenografts and that mRNA and protein expression of lipid transporters is enhanced in bone metastatic disease when compared with primary, localized prostate cancer. Our findings provide a strong rationale to investigate lipid uptake as a therapeutic cotarget in the fight against advanced prostate cancer in combination with inhibitors of lipogenesis to delay disease progression and metastasis. Implications: Prostate cancer exhibits metabolic plasticity in acquiring lipids from uptake and lipogenesis at different disease stages, indicating potential therapeutic benefit by cotargeting lipid supply.

The processes regulating tumor metastasis are multivariate and complex. Redox regulation of the tumor phenotype by GPx2 knockdown (KD) in breast cancer led us to uncover dramatic effects on spontaneous metastasis. Analysis of single cell RNA sequencing (scRNAseq) data from GPx2 KD tumor and control tumor, revealed that both tumors were comprised of several luminal-like tumor cell clusters and one mesenchymal-like cell cluster (cluster 3). Notably, GPx2 KD promoted a significant increase in the size of mesenchymal cells (cluster 3) relative to control, which might be due to the stimulation of epithelial-to-mesenchymal transition (EMT) in response to GPx2 loss. In support of this view, GPx2 KD stimulated an increase in mRNA expression of basal/mesenchymal (KRT5, KRT14, KRT17, Vimentin, Twist1, Twist2, CDH2) genes and a decrease in mRNA expression of epithelial/luminal (Cldn7 and Epcam) genes, especially in cluster 3. Moreover, GPx2 KD upregulated mRNA expression of basal/mesenchymal (Twist2, CDH2, and KRT14) genes in most luminal-like clusters expressing epithelial/luminal (Epcam, Cldn3/7, CDH1, KRT8/18) genes, implying these clusters may be undergoing EMT transition in a hybrid epithelial/mesenchymal state in response to GPx2 loss. Validation of these data in cell lines and tumors showed that GPx2 KD dramatically enhanced EMT via activation of ROS/HIF1α-mediated signaling. Importantly, these effects were reversed by GPx2 re-expression or HIF1α inhibition, which was capable of suppressing EMT and metastasis. Collectively, these results indicate that GPx2 loss promotes breast cancer metastasis by stimulating EMT due to HIF1α signaling, highlighting the impact of GPx2 and HIF1 on therapeutic intervention in metastasis. Citation Format: Zuen Ren, Huizhi Liang, Malindrie Dharmaratne, Miriam Morales-Gallego, Atefeh Taherian Fard, Jessica Mar, Kimita Suyama, Outhiriaradjou Benard, Michael B. Prystowsky, Larry Norton, Rachel B. Hazan. Loss of glutathione peroxidase 2 promotes epithelial to mesenchymal transition and breast cancer metastasis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 968.

This paper presents the dynamics and control of a new wind power plant configuration where multiple turbines are connected to a shared drivetrain. A hydrostatic transmission system is utilized to collect and transfer the harvested mechanical energy of individual turbines to the generator. The interconnected wind turbines are controlled individually to achieve maximum power point tracking (MPPT) while regulating the generator speed. In the first control loop, the pump displacement is adjusted accordingly to satisfy the MPPT. In the second control loop, the electrical load on the generator is controlled to balance the torque and reach a stable frequency operation. Simulation results show the performance of the control system operation. It also demonstrates an increase and improved smoothness of a power generation profile.